Enhanced ISAR imaging by exploiting the continuity of the target scene
This paper presents a novel inverse synthetic aperture radar (ISAR) imaging method by exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced target images within a Bayesian framework. A simplified radar system is utilized by transmitting the sparse probing freque...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2014
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| Online Access: | https://hdl.handle.net/10356/104851 http://hdl.handle.net/10220/20347 |
| _version_ | 1811679434358390784 |
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| author | Zhao, Lifan Wang, Lu Bi, Guoan Wan, Chunru Yang, Lei |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Zhao, Lifan Wang, Lu Bi, Guoan Wan, Chunru Yang, Lei |
| author_sort | Zhao, Lifan |
| collection | NTU |
| description | This paper presents a novel inverse synthetic aperture radar (ISAR) imaging method by exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced target images within a Bayesian framework. A simplified radar system is utilized by transmitting the sparse probing frequency signal, where the ISAR imaging problem can be converted to deal with underdetermined linear inverse scattering. Following the Bayesian compressive sensing (BCS) theory, a hierarchical Bayesian prior is employed to model the scatterers in the range-Doppler plane. In contrast to the independent prior on each scatterer in the conventional BCS, a correlated prior is proposed to statistically encourage the continuity structure of the scatterers in the target region. To overcome the intractability of the posterior distribution, the Gibbs sampling strategy is used for Bayesian inference. The parameters of the signal model are inferred efficiently from samples obtained by the Gibbs sampler. Because the proposed method is a data-driven learning process, the tedious parameter tuning process required by the convex optimization-based approaches can be avoided. Both the synthetic and the experimental results demonstrate that the proposed algorithm can achieve substantial improvements in the scenarios of limited measurements and low signal-to-noise ratio compared with other reported algorithms for ISAR imaging problems. |
| first_indexed | 2024-10-01T03:09:06Z |
| format | Journal Article |
| id | ntu-10356/104851 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:09:06Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/1048512020-03-07T14:00:36Z Enhanced ISAR imaging by exploiting the continuity of the target scene Zhao, Lifan Wang, Lu Bi, Guoan Wan, Chunru Yang, Lei School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electric apparatus and materials This paper presents a novel inverse synthetic aperture radar (ISAR) imaging method by exploiting the inherent continuity of the scatterers on the target scene to obtain enhanced target images within a Bayesian framework. A simplified radar system is utilized by transmitting the sparse probing frequency signal, where the ISAR imaging problem can be converted to deal with underdetermined linear inverse scattering. Following the Bayesian compressive sensing (BCS) theory, a hierarchical Bayesian prior is employed to model the scatterers in the range-Doppler plane. In contrast to the independent prior on each scatterer in the conventional BCS, a correlated prior is proposed to statistically encourage the continuity structure of the scatterers in the target region. To overcome the intractability of the posterior distribution, the Gibbs sampling strategy is used for Bayesian inference. The parameters of the signal model are inferred efficiently from samples obtained by the Gibbs sampler. Because the proposed method is a data-driven learning process, the tedious parameter tuning process required by the convex optimization-based approaches can be avoided. Both the synthetic and the experimental results demonstrate that the proposed algorithm can achieve substantial improvements in the scenarios of limited measurements and low signal-to-noise ratio compared with other reported algorithms for ISAR imaging problems. Accepted version 2014-08-19T08:58:02Z 2019-12-06T21:41:13Z 2014-08-19T08:58:02Z 2019-12-06T21:41:13Z 2013 2013 Journal Article Wang, L., Zhao, L., Bi, G., Wan, C., & Yang, L. (2013). Enhanced ISAR Imaging by Exploiting the Continuity of the Target Scene. IEEE Transactions on Geoscience and Remote Sensing, 52(9), 5736 - 5750. https://hdl.handle.net/10356/104851 http://hdl.handle.net/10220/20347 10.1109/TGRS.2013.2292074 en IEEE transactions on geoscience and remote sensing © Copyright 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TGRS.2013.2292074]. 14 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Electric apparatus and materials Zhao, Lifan Wang, Lu Bi, Guoan Wan, Chunru Yang, Lei Enhanced ISAR imaging by exploiting the continuity of the target scene |
| title | Enhanced ISAR imaging by exploiting the continuity of the target scene |
| title_full | Enhanced ISAR imaging by exploiting the continuity of the target scene |
| title_fullStr | Enhanced ISAR imaging by exploiting the continuity of the target scene |
| title_full_unstemmed | Enhanced ISAR imaging by exploiting the continuity of the target scene |
| title_short | Enhanced ISAR imaging by exploiting the continuity of the target scene |
| title_sort | enhanced isar imaging by exploiting the continuity of the target scene |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Electric apparatus and materials |
| url | https://hdl.handle.net/10356/104851 http://hdl.handle.net/10220/20347 |
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